We have focussed on developing datasets for gene expression using RNA-Seq, which allows us to apply a standard set of methods to a variety of model systems. Using this approach we have contributed to a number of different studies. One of the most interesting areas is in the application to the human brain, where we have a large series of brains with information on both genetic variability and gene expression. Our data has been used in many studies to determine whether a nominated genetic variant associated with a given disease or other phenotype, has a proximal biological effect on gene expression. Our major focus on the past year has been to understand the relationship between aging and gene expression. We have found that, in the brain, there are many genes for synaptic proteins that show declines with aging. While some of this signal is driven by changes in cell number, there is an additive signal for gene expression and cellularity. This observation holds across several brain regions, as demonstrated by analysis of publicly available GTEX data. We are currently trying to confirm these results in a larger follow up cohort of 250 brains.